Analysis of Potential Issues in Real-Time Collaborative Document Editor Architecture

Failure Modes

1. WebSocket Connection Failures

Issue: Users may experience interruptions due to unstable networks or server load balancer timeouts. Solution: Implement automatic reconnection logic with exponential backoff on the client side. Additionally, maintain an operational state in IndexedDB for offline operations, then sync when reconnected. Enhanced connection monitoring with heartbeat messages can detect failures early. Trade-offs: Increased client complexity and potential duplication of operations during reconnects that need reconciliation.

2. Database Single Point of Failure

Issue: The primary PostgreSQL database represents a single point of failure. Solution: Set up a synchronous standby replica in a different availability zone that can take over immediately. Implement database connection pooling to manage connections efficiently. Use a circuit breaker pattern to handle database failures gracefully. Trade-offs: Synchronous replication adds some latency but provides stronger data consistency guarantees.

3. Redis Cache Failure

Issue: Redis failure could lead to session data loss and user disruption. Solution: Deploy Redis in a clustered configuration with replication. Implement a cache warming strategy to speed up recovery. Fall back to database lookups for critical data. Trade-offs: Increased infrastructure complexity but improved resilience.

4. Split-Brain Scenario

Issue: During network partitions, different server instances might accept conflicting changes to the same document. Solution: Implement a consensus protocol like Raft to decide which server instance is the leader during partitions. Define a conflict resolution policy with explicit user notification. Implement partition detection using distributed coordination services like ZooKeeper. Trade-offs: Increased system complexity but improved consistency during network issues.

5. API Server Crashes

Issue: Unexpected crashes could cause in-flight changes to be lost. Solution: Implement an operational change queue that persists pending operations to disk. Persist document changes to a write-ahead log before acknowledging them. Implement a document versioning system to allow reconstruction of document state after server restarts. Trade-offs: Increased storage requirements but improved data integrity.

Race Conditions

1. Concurrent Edit Conflicts

Issue: Last-write-wins with timestamps can lead to data loss if two users edit the same paragraph at the same time. Solution: Implement operational transformation (OT) or conflict-free replicated data types (CRDTs) to handle concurrent edits intelligently. These algorithms can merge changes without data loss. Additionally, implement an undo/redo mechanism with branching to allow users to revert changes if needed. Trade-offs: Increased computational complexity but significantly improved user experience by preserving all edits.

2. Timestamp Skew

Issue: Client clock skew can cause conflict resolution inconsistencies. Solution: Use vector clocks instead of simple timestamps to establish partial ordering of events. Record timestamps from multiple clients and servers. Implement a bounded clock skew tolerance with a hybrid logical clock mechanism. Trade-offs: Increased complexity but correct resolution of concurrent edits despite clock skew.

3. Cross-Server Synchronization Delay

Issue: During the 2-second polling interval, servers might have outdated document states. Solution: Implement an inter-server communication mechanism using a message queue like RabbitMQ or Apache Kafka. Changes should be fanned out to all servers immediately rather than relying on polling. For truly real-time consistency, maintain server-to-server WebSocket connections. Trade-offs: Increased infrastructure complexity but reduced synchronization delay.

4. Storage Race Condition

Issue: The mechanism of saving full HTML snapshots every 30 seconds could overwrite more recent changes. Solution: Implement an operational log that records each change as it occurs. The snapshot should only be considered a savepoint, not the sole storage mechanism. Create snapshots only when the document is idle for at least 30 seconds. Store snapshots as differential patches instead of full HTML. Trade-offs: Increased storage complexity but reduced risk of data loss.

Scaling Bottlenecks

1. Database Connection Limits

Issue: As the system scales, the primary PostgreSQL database will become a bottleneck due to connection limits. Solution: Implement database connection pooling. Use read replicas for read-heavy operations. Consider partitioning the database by organization ID to distribute the load. Trade-offs: Increased complexity but improved throughput and reduced contention.

2. Cross-Server Broadcasting Inefficiency

Issue: The current polling-based cross-server synchronization will become inefficient at scale. Solution: Use a distributed message queue (RabbitMQ, Apache Kafka) for inter-server communication. When a change occurs, fan it out to all servers via the message queue rather than relying on polling. Implement an event-driven architecture for change propagation. Trade-offs: Increased infrastructure complexity but reduced polling overhead and improved real-time consistency.

3. Memory Usage

Issue: Storing full HTML snapshots every 30 seconds consumes significant memory. Solution: Store only changes in a sequential log structure. Reconstruct document state on demand. Implement a retention policy where only recent snapshots are kept in memory. Trade-offs: Increased complexity but reduced storage requirements and improved performance.

4. Connection Scaling

Issue: As documents become popular, a single server instance might be overwhelmed by WebSocket connections. Solution: Implement connection draining to redistribute connections when servers are under heavy load. Consider sharding documents across multiple servers based on document ID rather than server count alone. Implement a pub/sub pattern for broadcasting changes to clients. Trade-offs: Increased complexity but improved load distribution and scalability.

5. CDN Caching Issues

Issue: Caching API responses for 5 minutes could lead to stale document data. Solution: Implement cache invalidation via publish-subscribe when documents change. Set shorter cache TTLs for frequently changing documents, longer for stable ones. Implement conditional requests with ETags to reduce stale data risk. Trade-offs: Increased complexity but improved data freshness.

Conclusion

This architecture analysis reveals several critical issues that need to be addressed. The most critical are the potential data loss from last-write-wins conflict resolution, the single point of failure in the database, and the race conditions introduced by client clock skew.

Implementing operational transformation or CRDTs should be a top priority to ensure data consistency. The database layer needs high availability configuration, and the inter-server communication should be moved from polling to event-driven mechanisms.

For scaling, document sharding strategies and moving to event-driven cross-server synchronization will be essential. The architecture is generally sound but requires these enhancements to be production-ready.